The objective of this paper is to design an autopilot system for unmanned aerial vehicle (UAV) to control the speed and altitude using electronic throttle control system (ETCS) and elevator, respectively. A DC servo motor is used for designing of ETCS to control the throttle position for appropriate amount of air mass flow. Artificial Intelligence (AI)-based controllers such as fuzzy logic PD, fuzzy logic PD + I, self-tuning fuzzy logic PID (STF-PID) controller and fuzzy logic-based sliding mode adaptive controller (FLSMAC) are designed for stable autopilot system and are compared with conventional PI controller. The target of throttle, speed and altitude controls are to achieve a wide range of air speed, improved energy efficiency and fuel economy with reduced pollutant emission. The energy efficiency using specific energy rate per velocity of UAV is also presented in this paper.

Delay has a significant role to play in the implementation of the predictive current control scheme as large amount of calculations are involved. Compensating delay in the predictive current controller design can lead to an improved load current total harmonic distortion (THD) and also an increased switching frequency. Minimization of switching frequency while maintaining the lower value of load current THD requires multiple objective optimization which is achieved by optimizing a single objective function, constructed using weighting factors as a linear combination of individual objective function. The effect of weighting factor on the switching frequency minimization and the current tracking error with delay compensation for the two level voltage source inverter (VSI) are investigated in this paper. The outcomes of the predictive current control using an optimized weighting factor which is calculated using branch and bound algorithm with the delay compensation are compared with the PWM based current control scheme. The experimental tests are conducted on a 2.2 kW VSI to verify the simulation observations.

Among the numerous direct torque control techniques, the finite-state predictive torque control (FS-PTC) has emerged as a powerful alternative as it offers the fast dynamic response and the flexibility to optimize multiple objectives simultaneously. However, the implementation of FS-PTC for multiple objectives optimization requires the optimization of a single objective function, which is constructed using weighting factors asa linear combination of individual objective functions. Traditionally, the weighting factors are determined through a non-trivial process, which is a complex and time-consuming task. In an effort to avoid the timeconsuming task of weighting factor selection, this paper aims at replacing the weighting factor calculation with a systematic fuzzy multiple-criteria decision making in which the individual objective functions may have equalor varying degrees of importance. As a result the weighting factor calculation can be completely avoided. The simulation and experimental tests are conducted on a 2.2 kW induction motor drive to validate the proposed approach. The result outcomes are compared with the conventional predictive torque control (PTC) using weighting factors on the same experimental platform.